Abstract
The significant developments in solar photovoltaic (PV) technology have led to a strong push toward the development of new Maximum Power Point Tracking (MPPT) methods. Hence, various MPPT techniques have been applied to enhance the efficiency of PV energy. In addition, metaheuristic algorithms are widely used in various scientific and technical fields for problem solving purposes. Indeed, a majority of these techniques are inspired by natural phenomena, such as physical laws or biological processes. In the present study, the effectiveness of a smart MPPT technique utilizing Particle Swarm Optimization (PSO) has been evaluated, to enhance the efficiency of the photovoltaic system. To achieve this, a mathematical model was developed and implemented within the MATLAB/SIMULINK environment. PSIM tools were then used to verify and analyze the results. The findings obtained indicated that the high similarity of optimization in terms of maximum photovoltaic generator power, with an error of less than 1.8%.   Recent advancements in solar photovoltaic (PV) technology have led to a growing focus on improving Maximum Power Point Tracking (MPPT) techniques. Various MPPT methods have been developed to enhance the efficiency of PV systems. Additionally, metaheuristic algorithms, inspired by natural phenomena such as biological processes or physical laws, are widely used across scientific and engineering fields for optimization tasks. In this study, the performance of a novel MPPT technique based on Particle Swarm Optimization (PSO) was evaluated to improve the efficiency of photovoltaic systems. A mathematical model was developed and implemented in the MATLAB/SIMULINK environment, and the results were further validated using PSIM tools. The findings show that the proposed PSO-based MPPT technique achieves a high degree of optimization, with an error of less than 1.8% in terms of maximum photovoltaic power output.

Key words: Photovoltaic, MPPT, Optimization, Metaheuristic, PSO, MATLAB/SIMULINK, PSIM